Amazon SEO: Analyze Amazon keyword weight though A9 algorithm

2019-12-26
Reverse ASIN Keyword
 
The keyword on SellerSprite - Reverse ASIN Keyword is the traffic word of this ASIN. The higher the keyword ranks, the more exposure that the keyword brings to this ASIN.
 
For example, if your competitor is a coffee mug, customers search for “coffee mug” and see the product on page 3. This keyword is a traffic word of the competitor.
 
Customers searching for "coffee warmer" may also see the product, then this keyword is a traffic word as well, because it brings exposure to the competing product.
 
Such as: B0773WG6NK
 


From the perspective of Amazon A9 Algorithm
 
How does A9 evaluate the relevance of Search Phrase to your product?
 
As is known to all, Amazon search results are sorted and filtered by product relevance.
 
For example, if you search for “iPhone charger”, the first three pages are mostly for iPhone charger cable, a small number for iPhone 8 chargers and power bank, because A9 Algorithm considers this word to be most relevant to the cable.
 
When calculating the relevance, a core weight is the sales volume. A9 Algorithm found that most customers who searched for “iPhone charger” eventually bought some cable, so the cable is overwhelmingly more heavily weighted than the charger and ranked higher.
 
If customers purchase though keyword search, the keyword relevance and weight are the highest. That’s why one of the top traffic words for a Bose headset is “SONY wh-1000xm2 headset keyword”, because “Frequently bought together” of this Sony headset has Bose headphones, leading to Bose headphones appear after searching “sony wh-1000xm2”.
 
View the traffic word for a Bose headset: B0756GB78C



Search “sony wh-1000xm2” on Amazon:
 


Tag Weight
 
A9 Algorithm will build a profile for each ASIN, that is, mark various tags(assign keywords), and assign the weight to tags(keyword weight), so that when customers search a keyword, it will sort based on the keyword weight.    

The known keyword weight:
 
Lising keyword layout:
Whether it appears in the Title?
Whether it appears in Search Term?
Whether it appears in Bullet Point and Description?
Whether it appears in Review?
Whether it appears in Q/A?
 
Listing weight:
Listing launch time
Listing sales volume
Listing rating
Listing price
Listing category and relevance
 
User associated behavior:
Whether users will click on your product after searching for keywords?
Whether users will buy your product after searching for keywords?
Whether users will see your product after viewing a product?
Whether users will buy your product after viewing a product?
Whether users will buy your product after purchasing a product?
How long will users stay on your Listing?
 
There are far more known keyword weights than the ones I listed above, and traffic words are the ones that are weighted to show your product to users.
It is sorted by exposure, which is the “Weight” on SellerSprite.
 
This also shows that traffic words are not necessarily the keyword on Title, or Search Term, but the keyword that brings the real exposure to your product.
 
If SellerSprite recommends you 100 keywords, then the top 10 must be the most exposed search term in the last six months.
 
But when calculating the exposure, even on the same page, whether your ASIN is at the top or bottom of the page, the weight is not the same.
 
SellerSprite calculated is the most exposed keywords (search terms), but the keywords that bring the most exposure may not be the most clicked keywords (click words), also may not be the most purchased keywords (purchase words).
 
Because the traffic words are not keywords extracted from the product Title or Search Term, but keywords extracted from the user's search behavior, so when doing Listing optimization, you should select keywords that suitable for your Listing based on the traffic word of competitive product, and penetrate them into your product Listing; set the optimal keywords in the URL when doing outside promoting; and place the traffic words of competitive products when doing CPC advertisement.